The reliability of insulation systems is a major requirement of any power apparatus. The incidence of minor flaws and irregularities such as voids,
surface imperfections etc, in insulation systems is however inevitable and leads to partial discharges (PD). Classification of PD patterns plays an important
role during manufacturing and on-site assessment of power apparatus. The innovative trend of using artificial neural network towards classification of PD
patterns is perceptible. A novel method for the classification of PD patterns using the original probabilistic neural network (PNN) and its variation has been
proposed and implemented in this work. The classification of single-type insulation defects such as voids, surface discharges and corona has been
considered primarily. The efficacy and merits of PNN and its adaptive version over that of the back propagation algorithm based feed forward neural network
has been established through exhaustive comparisons on the performance of the neural networks in PD pattern classification task.